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Autocratic Audiences and Linguistic Complexity

Published online by Cambridge University Press:  28 October 2025

Nikita Khokhlov
Affiliation:
School of Politics and International Relations, University College Dublin, Dublin, Ireland
Alexander Baturo*
Affiliation:
School of Law and Government, Dublin City University, Dublin, Ireland
*
Corresponding author: Alexander Baturo; Email: alex.baturo@dcu.ie
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Abstract

Making policy speeches is a major activity of authoritarian elites, yet we know surprisingly little about their incentives to be understood by constituents, and whether more effective communicators are rewarded. While many authoritarian actors care little about their audience and speak tediously, we argue that, in the service of legitimation and co-optation, simpler, more effective communication is required in protest-prone regions with lower regime support. Because such regions often have more developed economies and educated populations, paradoxically, this results in the opposite dynamics to that under democracy, where simpler speech is addressed at less educated, poorer constituents. Drawing on data from Russian governors’ major policy addresses and social media posts, and supplementing it with federal parliamentary speeches, we find that the linguistic complexity of elites reflects their audiences; elites also reduce it when their strategic context changes. In turn, more effective communicators are promoted. Our findings contribute to an understanding of authoritarian co-optation, elite incentives, responsiveness, and propaganda.

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Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use.
Copyright
© The Author(s), 2025. Published by Cambridge University Press
Figure 0

Figure 1. Easy and difficult speeches: Keyness results.Note: analyses for the top-10 ranked (easy) and bottom-10 (difficult) addresses. Tokens translated from Russian are on the right subplot.

Figure 1

Table 1. Linguistic complexity of governor annual addresses

Figure 2

Figure 2. The effects of main variables on speech complexity.Note: estimated following Model 6, Table 1 (upper), and Models 6 and 8, Table 2 (lower subplot), and include 95% confidence intervals.

Figure 3

Table 2. Linguistic complexity of State Duma deputies’ speeches

Figure 4

Figure 3. Speech complexity and governor transfer to different regions.Note: the lines display predicted values of the ARI as the dependent variable and the respective regional indicator as the explanatory variable. MO: Republic of Mordovia; SAM: Samara; AMU: Amur; PRI: Primorsky Krai; SAK: Sakhalin; MOW: Moscow City; TYU: Tyumen.

Figure 5

Figure 4. Change in text complexity following protest onset and war mobilization announcement.Note: the results are based on interrupted time-series analyses for two events (in columns) and two groups of regions (in rows). Treatment groups are regions with UR vote shares below 50 per cent (upper) and high-protest regions (bottom). The data are from Khokhlov (2024).

Figure 6

Figure 5. Regression discontinuity in time results.Note: Estimated for the −10 to 10 per cent bandwidth of the vote share margin on the sample of dual Duma candidates, the third and fourth convocations; the graph displays average ARI scores during the first year following the election, with the size weighted by the number of MPs elected under particular margins.

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